There exists a wide variety of diagnostic systems for combustion engines generally designed to detect present and future failures of such engines. Exemplarily, U.S. Pat. No. 7,403,850 to Boutin et al teaches an “automated fault diagnostic system” that “operates on engine-compressor sets with one vibration sensor per sub-group of engine cylinders and one sensor per compressor cylinder. Vibration signals linked to crankshaft phase angle windows (“VT”) mark various engine events and compressor events. In data-acquisition-learning mode, VT is stored for each engine and compressor event per operating load condition, statistical process control (SPC) theory identifies alarm threshold bands. Operator input-overrides are permitted. If no baseline data is stored, the system automatically enters the learn mode. To monitor, current VT are obtained and current load condition is matched to the earlier load set and alarms issue linking predetermined engine or compressor event to the over-under VT. Baseline data, SPC analysis, alarms and monitoring are set for crankcase flow, engine cylinder exhaust temperatures, ignition system diagnostic messages. Compressor performance alarms use suction and discharge temperatures and pressures.” Thusly, Boutin's system utilizes vibration signals linked to the crankshaft phase angle; in the data-acquisition-learning mode it identifies alarm threshold bands using the SPC theory.
Another example of such systems is “A system and method for detecting and determining knocking in an internal combustion engine in which various kinds of signals (engine vibrations, engine revolution speed, opening angle of a throttle valve, crank angle, ignition crank angle, and so on) are weighted by means of weight vectors so as to form a knocking decision signal. The knocking decision signal is compared with a reference signal so as to determine whether the knocking is occurring. The correctness or incorrectness of the determination result is determined on the basis of a highly accurate knocking detecting information provided by means of an external test device. The weight vectors are corrected in a direction in which the percentage of correct answers increases under the various engine operating conditions. The weight vectors thus corrected and learned are used to carry out highly accurate knocking detection under varying engine operating conditions. The process can be repeated as a part of normal servicing so as to compensate for the effects of aging” described in U.S. Pat. No. 4,899,710 to Takahashi. His system therefore analyses engine vibrations, crank angle, ignition crank angle, etc.
U.S. Pat. No. 5,745,382 to Vilim et al discloses “A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural, network output to the industrial process signals to evaluate the operating state of the industrial process.” Consequently, Vilim's system employs the ‘neural network’ approach for evaluation of the operating state of the device.
Another U.S. Pat. No. 6,456,928 to Johnson proposes “Methods and devices for detecting and predicting parameter deviations and isolating failure modes in systems that are subject to failure. In a preferred embodiment, methods are provided for use with engines, including aircraft, automobile, and industrial combustion engines. However, numerous other applications are contemplated. Such engines may be described as having monitor points having current parameter values, where the monitor points may correspond to single physical sensors or to virtual or inferred monitor points having parameter values derived from multiple sensors. Acceptable ranges, limits, and values for each of the monitor point parameters may be provided for use with the present invention. Parameters lying outside of the acceptable ranges may be said to be in deviation. Ambiguity groups, including one or more failure modes or physical causes of the parameter deviations may also be provided. Parameter deviations, after optional filtering, may generate deviation signals which may be followed by analysis of the ambiguity groups to isolate the failure mode or modes causing the deviation. Courses of engine operation ameliorating the failure mode may be suggested. Methods are also provided for projecting current trends into the future to predict deviations and isolate failure modes early, prior to actual occurrence. One preferred use for the methods is early detection and isolation of faults in aircraft engines, leading to corrective action including early preventative maintenance.” In the instant inventors' opinion, the systems and methods, mentioned in U.S. Pat. No. 6,456,928, represent an advantageous approach of analysis that allows identifying the failure mode or modes causing the deviation from the normal regime.
Since this area of technology is vitally important not only for carmakers, but also for aviation, numerous research works have been conducted by respective organizations and companies, including those financed by NASA. Report NASA/CR-2002-211485 46 particularly states:
“The application of vibration monitoring is an obvious area of interest in the subject of engine, monitoring and diagnostics. Certain engine conditions such as bearing wear are best detected through vibration monitoring. The AGATE PMDS hardware was designed to optionally take information from a vibration sensor mounted directly on the engine, process the vibration data, and determine prognostics from that data. A university-led study performed under the Honeywell AGATE program has shown that, for piston engines, vibration monitoring can be used to detect engine conditions such as bearing wear. However, the team discovered that it was not sufficient to analyze the frequency spectrum of the vibration data as in traditional vibration monitoring methods, but rather to use a direct sample of the vibration time signal. This preliminary study used 5000 samples/second for one second of engine operation in order to detect engine conditions sufficient for prognostic prediction. Further research and development will be necessary before such optional vibration data can be used in a reliable fashion for engine prognostics . . . . The PMDS concept is intended to independently monitor the performance of the engine, providing continuous status to the pilot along with warnings if necessary. Specific sections of this data would be available to ground maintenance personnel via a special interface. The inputs to the PMDS include the digital engine controller sensors and other sensors. At its present stage of development, the PMDS monitors and records engine parameters and stores them into an engine history database for subsequent processing by off-line diagnostic algorithms . . . .” Although no tangible results have been shown, an important conclusion can be made: the Fourier series approach, which the vibration analysis has been traditionally based on, is recognized insufficient for further advancement in engine vibro-diagnostics.
The aforementioned systems and methods are also associated with certain deficiencies and shortcomings. Their vibration analysis primarily aims at the monitoring of aging and wear of mechanisms operatively engaged with each other. In addition, a majority of the methods encompass the combustion engine signal processing based on several assumptions that do not sufficiently reflect the real non-stationary nature of the signals and engine processes that cause such signals.
A large number of specialists have made numerous attempts to analyze the signals derived from operation of particular mechanisms (e.g. piston rings), which attempts have eventually failed due to the fact that their signals are just lost in the noise of the engine. This has lead to a common opinion of analyzing only revolving parts of the engine by means of the spectral analysis that implies the assumption of stationary nature of the signals.
Another approach has been taken in a second group of the diagnostic methods that is based on analyzing the signals in time. This approach requires a high qualification of the specialists, and is also characterized with instability and dependability on the place and manner of contact.